This study addressed the unique challenges of promoting physical activity among older adults with chronic diseases in the informal workforce, who often face physical limitations and lack structured schedules or access to workplace wellness programs. To address this gap, a mobile application based on Social Cognitive Theory was developed to encourage physical activity among older adults with hypertension. Guided by the ADDIE model, the app's development included a needs assessment involving a survey of 90 individuals and a literature review. This informed the design of key features, such as educational content, a physical activity calendar, exercise location identification, a ranking and rewards system, and a discussion forum. Rigorous testing was conducted, including pilot testing (n=6), usability testing (n=90), and content validation by experts (n=5). The app was well-received by older adults with hypertension (n=90, mean age=66.7 y, 82% female), who particularly valued features promoting self-care and information on the benefits of physical activity. This theoretically driven app offers a promising solution to promote physical activity in this vulnerable population. Further research will evaluate its effectiveness and explore its wider applicability.

This pilot study explored the longitudinal relationship between daily dementia caregiving and physiological stress, assessed through heart rate variability (HRV) measured using a wearable device, among Korean American home care workers (HCWs). Fifteen HCWs wore the Ōura ring for 21 days, providing 14 days of valid nocturnal HRV data after baseline calibration. HRV was quantified as the root mean square of successive differences (RMSSD) derived from photoplethysmography (PPG)-based interbeat intervals. To minimize interindividual variability, HRV values were standardized within participants. Multilevel linear models examined within- and between-person associations of dementia caregiving (yes or no and total hours) with HRV, adjusting for covariates. All were women, and the majority of HCWs (86.96%) were unsure about the type of dementia their care recipients had. Of 210 potential daily observations, 198 (94.3%) were valid. HRV displayed substantial within-person day-to-day fluctuations (intraclass correlation = 0.20) despite relatively low baseline self-reported stress. Although dementia caregiving was not significantly associated with HRV, the estimated coefficients suggested small-to-moderate within-person effect sizes. These findings highlight the dynamic nature of physiological stress and underscore both the feasibility and limitations of nocturnal PPG-derived HRV for real-world stress assessment. The study provides preliminary methodological guidance for wearable-based HRV research in understudied caregiver populations.

This study evaluated the long-term effects of the ControlVit mobile application on clinical outcomes and patient empowerment in individuals with chronic heart failure (HF), 6 months after discontinuation of the app. In this open-label, randomized, controlled clinical trial, patients were assigned to either an intervention group receiving standard HF management plus the ControlVit app for 6 months or to a control group receiving standard care alone. After 6 months, app use was discontinued, and all participants were followed for an additional 6 months. The primary outcome was the composite of death or HF-related hospital readmission at 12 months, whereas secondary outcomes included changes in body weight, natriuretic peptide levels, New York Heart Association (NYHA) functional class, and patient empowerment, measured using the Empowerment Evaluation Scale (EES). A total of 140 patients were enrolled (28.6% women; mean age, 67 y). At 12 months, 8 patients (11.4%) in the intervention group and 3 patients (4.3%) in the control group experienced the primary outcome [relative risk (RR): 2.62; 95% CI: 0.73-9.52; P=.122). Empowerment levels were significantly higher among app users compared with controls (EES score: 4.68 ± 0.36 vs. 4.40 ± 1.91; P=.012). Use of SGLT2 inhibitors and ARNi increased significantly in both groups during follow-up (ControlVit: 60% to 85%, P<.001; Control: 51.4% to 75%, P<.001). Among outpatients with chronic HF participating in a structured HF program, the addition of a mobile telemonitoring application did not reduce mortality or HF readmissions 6 months after its discontinuation. However, the intervention was associated with sustained improvements in patient empowerment, suggesting a lasting behavioral impact beyond the period of active app use.

This case-control study develops a machine-learning (ML)-based fall-risk-prediction model for military-hospital inpatients, using electronic records (January 2020 to December 2023) from 11 military hospitals in South Korea. The dataset includes 1470 fall and 3478 nonfall cases, and 25 predictive factors are selected based on data completeness and previous studies. ML models, including logistic regression, decision trees, random forest (RF), and gradient boosting, are developed and evaluated using 10-fold cross-validation. The predictive performances of these models are compared with that of the Morse fall scale (MFS). RF exhibits superior performance, with accuracy, recall, F1-score, and area under the curve (AUC) of 0.76, 0.77, 0.76, and 0.83, respectively. The MFS exhibits a poor predictive capability of 0.02. The key predictive variables include the admission method (on foot), medical department (orthopedics), MFS score, activity status, and body mass index. The findings reveal the limitations of MFS and higher accuracies of ML models in predicting falls among young military inpatients, highlighting the suitability of ML models for improving fall-prevention strategies. Future research should integrate real-time patient conditions and key fall-related factors into prediction models and embed these into clinical decision-making systems.

The aging population, particularly women, faces complex biopsychosocial health and social challenges, necessitating whole-person approaches and innovative research methods to better understand health and well-being to guide effective interventions. The objective of this pilot comparative cross-sectional study was to examine whole-person health and resilience of older adult women ages 65 and older in the United States and Türkiye. Data were collected from adult women in the United States (n=25) and Türkiye (n=25) between March and August 2024. Whole-person health was evaluated using the MyStrengths+MyHealth application, and resilience was measured using the 14-item Resilience Scale. Data were analyzed using descriptive and inferential statistics to examine between-group differences. The average age for the US participants was 78 (SD=6.2), and the average age for the Turkish participants was 68 (SD=2.8). There were significant differences in average self-reported strengths (P<0.001), challenges (P<.001), and needs (P<.001) between US and Turkish participants. US participants had a higher mean Resilience score [88.0 (SD=5.3)] versus Turkish participants [74.9 (SD=14.7)] (P<.001). The study revealed that both groups had above-average resilience and had both common strengths and differences in health challenges and needs. These findings highlight the importance of prioritizing a whole-person health approach when developing strengths-based interventions to address the complex and diverse needs of older women.

The growing integration of the electronic health record in health systems has contributed to care improvements, yet is increasingly recognized as a source of stress and burnout for nurses. This study, conducted in a southeastern Veterans Affairs health system, used focus groups composed of 51 direct care nurses in shared governance councils practicing across inpatient and outpatient settings to assess their perspectives regarding documentation burden. Qualitative content analysis of focus group transcripts revealed 4 themes: poor usability, uncertainty, nonvalue added to patient care, and risk for error and liability. Fifteen subthemes were identified. Participants perceived much documentation as duplicative, nonimpactful on patient outcomes, and frequently driven by regulatory or quality reporting rather than clinical necessity. The findings reflect current literature and align with the American Nurses Informatics Association's Six Domains of Burden Framework, which underscores documentation burden as a modifiable, systems-level issue. Engaging direct care nurses through shared governance structures may support targeted solutions such as eliminating duplicate documentation, enhancing electronic documentation usability, and clarifying documentation standards. Health information systems redesign must focus on the safety and well-being of both nurses and patients to ensure the provision of quality nursing care and retention of a strong nursing workforce.

Effective handoff communication between the emergency department and inpatient units is essential for patient safety and nurse well-being. This project uses an innovative design strategy and generative artificial intelligence to improve the quality and consistency of information exchanged during nurse-to-nurse handoffs. By generating computer-based summaries, key patient details can be accurately conveyed, reducing the need for manual review and customization. Developed through a collaborative hackathon with frontline nurses, informaticists, and technology experts, the solution underwent rapid prototyping and provided real-time feedback to ensure alignment with clinical workflows. This approach improves efficiency, reduces the cognitive burden, and standardizes the transfer of critical information. This initiative minimizes information loss and supports nurses in delivering safe, high-quality care by strengthening communication during the transition from the emergency department to inpatient care. In addition, the project introduces the impact of artificial intelligence-assisted handoffs on nurse satisfaction and workflow efficiency, offering insights into how technology enhances clinical documentation and decision-making.

Ensuring patient adherence to treatments and lifestyle recommendations after discharge is crucial for effective postacute myocardial infarction (AMI) management. This study aimed to determine the impact of a mobile health application (mHealth) on treatment adherence, self-management, and quality of life in patients with their first-time AMI undergoing primary percutaneous coronary intervention. The randomized controlled study was conducted with 61 patients. The control group received standard discharge education with the educational booklet. In addition to these practices, the intervention group received mHealth support. They were followed up for 6 months postdischarge. Data were collected at the first, third, and sixth months. The patient information form, questioning compliance with lifestyle change recommendations, the Medical Adherence Report Scale, and the Myocardial Infarction Dimensional Assessment Scale were used in data collection. Compared to the control group, the patients in the intervention group had a better quality of life at all 3 follow-ups and better medication adherence at the sixth month. Also, it was determined that the patients in the intervention group had higher adherence with lifestyle recommendations regarding nutrition and physical exercise than in the control group (P<.05). mHealth increases their adherence with treatment, including medication and lifestyle change recommendations, in the long term. In addition to the education provided at discharge, using a mobile health application was found to effectively improve patients' adherence to treatment, including medication and lifestyle changes. Consequently, patients' quality of life improved during the 6-month follow-up period. Innovative approaches that support self-management, such as mHealth, are considered promising additions to AMI control.

Generative artificial intelligence offers personalized patient education, yet clinical inaccuracy and lack of theoretical grounding threaten health care safety. This study validates a "nurse-led" AI protocol for generating safe, theory-guided digital education materials based on Kolcaba's Comfort Theory. A methodological design established a three-stage iterative prompt engineering process (initial, clinical refinement, and theoretical alignment). Stroke, chronic kidney disease, and COPD served as case models. Quality was assessed via expert panel reviews (n=3) using the Content Validity Index and Ateşman's Readability Index. The protocol effectively mitigated "AI hallucinations." Theoretical integration ensured holistic alignment across comfort domains. Readability scores significantly improved from 48.5 to 66.8. High expert consensus (CVI: 0.93-0.95) demonstrated clinical safety, proving nursing expertise is a mandatory safety layer. This framework provides a replicable quality-control protocol for nurses as digital content curators, ensuring AI-generated materials are clinically safe and theoretically sound.

This study aimed to develop an interactive web-based program, "I Can Manage," to support nurse-led self-regulation in the management of Multiple Sclerosis (MS). An action research approach was adopted, utilizing the Spiral Technology Action Research model, which involves the stages of listen, plan, do, study, and act. The study was conducted with 16 nurses and persons with MS (pwMS). Data were collected through in-depth individual interviews and the System Usability Scale (SUS). In the "Listen" stage, existing programs were reviewed, and a descriptive phenomenological study was conducted. In the "Plan" stage, program features were designed based on self-regulation strategies. In the "Do" stage, these features were shared with participants and implemented. In the "Study" stage, participants reevaluated the program in terms of navigation and usability. In the "Act" stage, the program was presented to experts, and participants assessed it using the SUS. Themes that emerged included "emotions that need to be managed," "support required for self-management," "provision of individualized services," "barriers to self-management," and "enhancement of care quality." The Content Validity Index (CVI) was 0.96. The mean SUS scores were 90.50±6.93 (nurses) and 88.25±6.87 (pwMS). This study demonstrates how nurses can take leadership roles in identifying patient needs and developing effective web-based programs to enhance self-management and care quality.